Technical ReportPDF Available

CRC-P Integrated Home Energy Management System Project Report: Analysis of electricity consumption by hot water tank

Abstract and Figures

One of the main goals of the Cooperative Research Centre (CRC) Project, Integrated Smart Home Energy Management is to control and automate major electrical appliances to bring financial and environmental benefits to household owners. Successful implementation of the project on a large number of households can also assist DNSP and networks as a demand response tool in managing peak demand and over voltage problems due to increasing levels of rooftop PV penetration. In this preliminary report, Solar Analytics households whose electric hot water system is monitored through a separate electrical circuit are studied to discover the characteristics of electricity consumption by hot water tank. In addition, potential of electric hot water load shifting capacity to the times of peak excess rooftop PV generation and associated financial benefits are discovered. It is found that: • On average households have 17.3 kWh of daily electricity load (excluding electric hot water), 6 kWh of daily electric hot water load and 15.6 kWh of daily rooftop PV generation, exporting 56.8% of their generation. • On average there is 4.3 kWh of daily electric hot water load that can be provided by excess rooftop PV generation per household which roughly corresponds to household's 70% of daily electricity consumption by hot water tank. • On average, annual savings range between $48-$324 depending on household's electricity and hot water heating tariff: continuous, control 1 (tariff 31) and control 2 (tariff 33). In general, the savings are greater for SA households followed by QLD and NSW. The obtained results are only indicative of the current tariff offers provided by major retailers and may change as new offers and control schemes come into play. The report also carries a preliminary network level load shifting analysis which shows the promising potential of shifting aggregate electric hot water load to solar generation times for reducing excess rooftop PV generation. As the next step, different electric hot water load shifting strategies should be investigated and tested on the chosen households. This will validate the obtained results of utilizing excess rooftop PV generation on electric hot water load and the associated savings for the households.
Content may be subject to copyright.
Prepared by: Baran Yildiz
1
st
draft date: 11/06/2019
Project Report: Analysis of electricity consumption by hot
water tank
CRC-P Integrated Home Energy
Management System Project
Prepared by Baran Yildiz
15/06/2019
Executive Summary
One of the main goals of the CRC-P Integrated Smart Home Energy Management Project is
to control and automate major electrical appliances to bring financial and environmental
benefits to household owners. Successful implementation of the project on a large number
of households can also assist DNSP and networks as a demand response tool in managing
peak demand and over voltage problems due to increasing levels of rooftop PV penetration.
In this preliminary report, Solar Analytics households whose electric hot water system is
monitored through a separate electrical circuit are studied to discover the characteristics of
electricity consumption by hot water tank. In addition, potential of electric hot water load
shifting capacity to the times of peak excess rooftop PV generation and associated financial
benefits are discovered. It is found that:
On average households have 17.3 kWh of daily electricity load (excluding electric hot
water), 6 kWh of daily electric hot water load and 15.6 kWh of daily rooftop PV
generation, exporting 56.8% of their generation.
On average there is 4.3 kWh of daily electric hot water load that can be provided by
excess rooftop PV generation per household which roughly corresponds to
household’s 70% of daily electricity consumption by hot water tank.
On average, annual savings range between $48 - $324 depending on household’s
electricity and hot water heating tariff: continuous, control 1 (tariff 31) and control 2
(tariff 33). In general, the savings are greater for SA households followed by QLD and
NSW.
The obtained results are only indicative of the current tariff offers provided by major
retailers and may change as new offers and control schemes come into play. The report also
carries a preliminary network level load shifting analysis which shows the promising
potential of shifting aggregate electric hot water load to solar generation times for reducing
excess rooftop PV generation. As the next step, different electric hot water load shifting
strategies should be investigated and tested on the chosen households. This will validate the
obtained results of utilizing excess rooftop PV generation on electric hot water load and the
associated savings for the households.
Contents
1 Data-set summary .................................................................................................... 3
2 General insights ........................................................................................................ 3
2.1 Households by state .......................................................................................... 3
2.2 Annual average daily analysis ............................................................................ 4
2.3 Seasonal Daily and Weekday/Weekend Analysis ................................................ 6
2.4 State-wide & seasonal analysis .......................................................................... 8
2.5 Time of day/hourly analysis ............................................................................. 10
3 Analysis of electricity consumption by hot water tank ............................................. 13
3.1 Household’s perspective .................................................................................. 15
3.1.1 Electricity consumption by hot water tank ................................................ 15
3.1.2 Using excess PV generation to heat water ................................................ 17
3.1.3 Economic analysis .................................................................................... 20
3.2 Network’s perspective ..................................................................................... 26
3.2.1 Case Study for South Australia (SA) Households ........................................ 27
3.2.2 Queensland case study ............................................................................. 29
4 Conclusions & Future work ..................................................................................... 31
References ..................................................................................................................... 33
Appendix. A ................................................................................................................... 33
Appendix. B ................................................................................................................... 34
Appendix. C ................................................................................................................... 35
Appendix. D ................................................................................................................... 36
1 Data-set summary
For the analysis, residential Solar Analytics sites which had dedicated circuit for electric hot
water heating were selected. To understand seasonal variations, each household had at
least one year of un-intermittent data for household electric load (excluding hot water), PV
generation and electricity consumption by hot water tank. Some households had highly
irregular consumption patterns which indicated the house may be a vacation house used for
short period of time. These households were also eliminated. Some households’ data need
to be pre-processed as their net load included their electricity consumption by hot water
tank even though they had separate circuit. To find more details on the data cleaning, please
refer to Appendix. A. The final analysed data set consists of:
353 households with 5 minutely load, PV and electricity consumption by hot water
tank
‘Load’ refers to household gross load excluding hot water
1 full calendar year March 2018- March 2019
2 General insights
2.1 Households by state
Below pie chart summarizes the analysed households by state. As seen, majority is from QLD
(70%), followed by NSW (~17%) and SA (~10).
Figure 1 Analysed households by state
2.2 Annual average daily analysis
This section presents the average daily consumption and generation statistics. The summary
statistics of the households are presented in below Table 1. The distribution of the average
daily household load, electricity consumption by hot water tank, gross PV generation and
excess PV generation (PV generation that is being exported) are given in below Figure 2.
Some key takes:
On average households use 17.3 kWh (exc. hot water) and 23.3 kWh (inc. hot water)
per day.
On average households have 6 kWh of electricity consumption by hot water tank per
day.
On average households generate 15.6 kWh energy per day and export 9.4 kWh to
grid (Average PV system size is 4.1kW).
On average households have 43.2% daily PV self-consumption rate. 70% of the
households are exporting at least 50% of their PV generation.
Table 1 Table of summary statistics
Daily Load (kWh)
(exc. hot water)
Daily Hot
water (kWh)
Daily PV
(kWh)
Daily Excess
Daily self-
consumption rate (%)
mean
17.3
6.0
15.6
43.2
min
5.8
1.2
1.8
14.3
max
47.5
16.3
25.6
94.7
25% percentile
12.2
3.9
12.1
30.9
75% percentile
20.8
7.4
19.6
53.2
Daily household load (exc. hot water)
Daily electricity consumption by hot water tank
Daily PV generation
Daily excess PV generation
Figure 2 Distribution of daily average household load, electricity consumption by hot water tank, gross PV generation and excess PV generation
2.3 Seasonal Daily and Weekday/Weekend
Analysis
This section analyses the variability in daily load, electricity consumption by hot water tank,
gross and excess PV generation across different seasons and weekday/weekends. 353
households each with 365 daily profiles ~130,000 daily profiles are analysed. Figure 3 below
presents the distribution of daily hot water, household load, gross and excess PV generation
across different seasons and weekdays (‘wday‘) and weekends (‘wend’) by box plots. More
details are presented in Appendix. B. Some key takes are:
Daily household consumption (exc. hot water) is significantly greater during summer
season. Notice that there are many days during summer where consumption goes
beyond 40kWh. This is most likely due to air conditioning load.
Household load during winter is only slightly greater than autumn and spring which
may indicate that electric space heating is not a dominant load for the analysed
households.
There are no significant differences in the distribution of household load between
weekdays and weekends except for Autumn where consumption is higher on the
weekends.
As expected, electricity consumption by hot water tank is highest during winter and
lowest during summer season. There is no significant difference in electricity
consumption by hot water tank between weekday & weekends
PV generation is highest during summer, very closely followed by spring. Number of
rainy/clear sky days (tropical summer in QLD), greater summer temperature and its
effect on PV generation are the reasons behind why the overall generation is very
close between two seasons, even though peak sunshine hours (PSH) are higher
during summer season.
Excess PV generation is highest during spring, followed by summer which is due to
smaller daily load during spring season. There are no remarkable differences in the
excess PV amount between weekday/weekends.
Figure 3 Distribution of seasonal daily and weekday/weekend household load, electricity consumption by hot water tank, gross and excess PV generation
2.4 State-wide & seasonal analysis
This section analyses daily household load, electricity consumption by hot water tank, gross
and excess PV generation between three states: QLD, NSW and SA across four seasons, as
shown in below Figure 4. Some key takes are summarized below:
Daily summer loads for QLD households are significantly greater, followed by NSW
and SA households. Households in NSW have higher household load during winter.
SA households have smaller consumption throughout all seasons.
There are no big differences in daily electric consumption by hot water tank
between states. The daily electricity consumption by the hot water tank is greater
during winter as previously shown.
SA households have the highest excess PV generation during summer, spring and
autumn. In winter, SA households have the smallest excess PV generation and QLD
households have highest excess PV generation. This may be due to the greater
reduction in PSH in winter for SA region.
Figure 4 Distribution of seasonal daily household load, electricity consumption by hot water tank, gross and excess PV generation across three states: QLD, NSW and SA
2.5 Time of day/hourly analysis
This section analyses the household load, electricity consumption by hot water tank, gross
and excess PV generation across the 24hours. Below box plots demonstrate the distribution
of household electricity load, electricity consumption by the hot water tank and excess PV
generation across 24 hours. The analysis is carried for each season and weekday/weekend.
Some key points are summarized below:
Weekday load profiles show the typical morning & afternoon/night-time peak
patterns for all seasons except for the summer season! During summer there are
higher loads from 13:00/14:00 onwards most likely due to air conditioning loads.
On the weekends, household loads are greater in the middle of the day as expected.
Summer weekends have much higher day time loads most likely due to air
conditioning loads.
Highest electric consumption by hot water tank is observed between 23:00-02:00,
for all seasons and both for weekday & weekends. This is mainly contributed by the
households whose hot water is on controlled load 1.
There is also a peak in electricity consumption by hot water tank between 7:00-
09:00 for weekdays and 9:00-12:00 for weekends. This pattern is most significant
during winter season, followed by autumn and spring. This is contributed by
households, whose electric how water is either on a control load 2 or continuous
tariff (more details in Section 3).
Excess PV generation is highest between 12:00-13:00 and 13:00-14:00 throughout all
seasons.
Figure 5 Household load across 24h analysed for seasons & weekday/weekend
Figure 6 Household electricity consumption by hot water tank across 24h analysed for seasons &
weekday/weekend
Figure 7 PV generation across 24h analysed for seasons & weekday/weekend
Figure 8 Excess PV generation across 24h analysed for seasons & weekday/weekend
3 Analysis of electricity consumption by
hot water tank
This section analyses household electricity consumption by the hot water tank. Aligned with
the project goals, the focus is finding the amount of electric hot water load that can be
shifted to periods of excess PV generation with the goal of improving PV self-consumption
rate. The implications of the shiftable electric hot water load are investigated from both
household’s and network’s perspective. Currently there are two types of controlled load
tariffs being offered for electric hot water heating. Control 1/Tariff 31 and Control 2/Tariff
33 (Controlled load 1 & 2 terms are used in NSW and Tariff 31/33 terms used in Queensland.
SA has one generic Controlled load term). Control 1/Tariff 31 allows heating for 8 hours per
day and is only eligible for medium to large size tanks which offer larger thermal storage
capacity, mostly owned by free standing households (250L < Tank Volume < 630L). Control
1/Tariff 31 offers cheapest rates for heating hot water. Smaller tanks with smaller storage
capacity (100L < Tank volume < 250L) can use Control 2/Tariff 33 since it allows heating 16
hours per day, minimizing the risk of running out of hot water. Control 2/Tariff 33 offer
higher rates than Control 1/Tariff 31 due to increased availability. Besides, regardless of the
tank volume, hot water system may be connected to one of the continuous tariff options
such as Flat tariff or Time of Use tariff which offers more expensive rates than the controlled
load options. Below Table 2 summarizes the timing of different control loads after
investigating the new tariff plans for major DNSPs for 2017-2020 & 2020-2025 periods,
(Ausgrid, 2018; Endeavour Energy, 2018; Energex, 2019b, 2019a; Ergon Energy, 2016; SA
Power Networks, 2017).
Table 2 Periods of different controlled load operations by DNSPs
Control 1/Tariff 31
Summer (1st Oct-1st Apr)
Winter (1st Apr - 1st Oct)
Ausgrid
9pm-6am
10pm-7am
Endeavour
10pm-7am
10pm-7am
Energex *
10pm-7am
10pm-7am
Ergon
10pm-7am
10pm-7am
SAPN*
11pm-7am & 10am-3pm
11pm-7am & 10am-3pm
* SAPN has plans to extend the control load hours to include 10am-3pm under their 'Solar Sponge'
strategy
* Energex has a new tariff promoting demand response/load shifting NTC7300 (similar to 'Solar
Sponge')
Control 2/Tariff 33 (available for 16 hours outside below window)
Summer (1st Oct-1st Apr)
Winter (1st Apr - 1st Oct)
Ausgrid
2pm-7pm *
5pm-8pm
Endeavour
5pm-8pm
5pm-8pm
Energex
4pm-8pm
4pm-8pm
Ergon
4pm-8pm
4pm-8pm
* The times change to 4pm-7pm during October.
SAPN does not offer control load 2
It should be emphasized that according to recent reports, SAPN and Energex are in trial for
implementing a new controlled load scheme for peak solar hours, where the overall strategy
is referred as ‘Solar Sponge’. As the name suggests, the controlled load offer now include
lower prices for the peak solar hours. Neither DNSP have provided much detail about their
new offerings, yet it is important to realize that this strategy is most likely to be taken by
other DNSPs as well with increasing levels of rooftop PV. The potential implications of this
new tariff regime on Solar Analytics customers is discussed in more detail under the
Networks Section.
The breakdown of the different electric hot water heating connection is given in below
Figure 9 for the Ausgrid customers (Ausgrid, 2016). Continuous (large tank) denotes the
households which have eligible tanks for control load 1 however they remain in a continuous
tariff and continuous (small tank)denotes the households which have smaller tanks which
may be eligible for controlled load 2 but remain in a continuous tariff. The chart includes
900,000 households who have electric water heating amongst the total 1,500,000
households connected in Ausgrid network (the rest of the 600,000-use gas & instantaneous
water heating). It is seen that 45% of the households are not on controlled load (11% with
large tanks and 34% with small tanks most likely in apartments) and 55% of the households
have controlled load (38% on control 1 and 17% on control 2)
Figure 9 Breakdown of electric hot water connection configuration of 900,000 Ausgrid customers with
electric water heaters
Question 1: If a household’s hot water (or any other appliance) is connected to any of the
above controlled load, what would be the physical control method to over-ride this? For
example, if Solar Analytics want to control the electric hot water heaters that are on Tariff
31 for QLD customers to utilize the excess solar generation better, what sort of connection
alterations will be required and what level of costs might be involved from the electrical
connection/wiring point of view. This would have an impact on the overall cost/benefit of
the load shifting product. Initial discussions with Solar Analytics Ops team indicated that
this is possible and implemented on two test sites. However, it needs more investigation in
terms of potential costs and for any potential regulatory concerns.
3.1 Household’s perspective
Using the excess rooftop solar generation for heating water can bring economic benefits for
households. This potential financial gain will depend on household’s hot water heating tariff,
hot water consumption and excess PV generation during the day as explained in more detail
in Section 3.1.3. There is also reduction in CO2 footprint associated to the increase of onsite
excess PV consumption brought by reduced losses.
3.1.1 Electricity consumption by hot water tank
Firstly, the households are analysed in terms of the block periods they use electrical hot
water heating. The choice for the four block periods below is to have a general
understanding of the timing:
Off-peak: 10pm-7am (control 1/tariff 31),
Morning: 7am-10am,
Solar times (peak excess periods): 10am-4pm,
Afternoon & night: 4pm-10pm
Figure 10 demonstrates the electricity consumption periods by hot water tank across
different periods of the day averaged across all households.
Figure 10 Average electricity consumption by hot water tank across different blocks during the day
Below Figure 11 presents the distribution for the percentage of electricity consumption by
hot water tank at different blocks during the day across households. Some key takes:
21% of the households are on control load 1/Tariff 31, as almost all electricity
consumption by hot water tank occurs during off peak hours 10pm-7am.
A small proportion (~4%) of the households use excess solar to heat at least 80% of their
hot water. A large proportion (~60%) have little electricity consumption by hot water
tank during solar hours (less than 20%).
Figure 11 Distribution of electricity consumption by hot water tank periods across households
Considering the controlled load tariff hours given in the above Table 2, the following Figure
12 breaks down the analysed Solar Analytics households in terms of their current hot water
tariff. Note that all the analysed households have medium-large residential PV systems
hence it is assumed that there are no apartments (hence the water tanks are all medium to
large size). Control load 2 households are found by their region and the restricted periods of
hot water electricity consumption: for example NSW households that have less than 2% (to
account for noise) of electricity consumption by hot water tank between 5pm-8pm during
winter and that are not already on control 1, are control 2 customers. The households that
are neither on control 1 or control 2 are on a continuous tariff.
Figure 12 Breakdown of electric hot water connection/tariffs of analysed Solar Analytics households
It is seen that 43% of the analysed Solar Analytics customers use continuous tariff for their
electric hot water heating and the remaining 57% are on a controlled load tariff (control 1
21%, control 2 36%). As it will be discussed in more detail in the economics section (Section
3.1.3) financial benefits of shifting hot water load to excess PV periods vary according to the
households’ electric hot water heating arrangement.
3.1.2 Using excess PV generation to heat water
This section analyses the amount of electric hot water load that can be provided by excess
PV generation. For every household, excess PV generation available for water heating is
calculated from 5 minutely resolution data. At this stage, the focus is finding the potential
volume (kWh) of shiftable hot water load to excess solar periods and the associated financial
benefits. Therefore, this section presents the most optimal case as it doesn’t consider the
inefficiencies that can be caused by the chosen method for shifting hot water load (such as
diverters, timers, custom control algorithms etc.).
Below Figure 13 shows the range of daily electric hot water heating (in %) which can be
provided by excess PV vs. percentage of households (analogous to a load duration curve).
Each line represents household’s average seasonal and weekday/weekend profiles. The
following Figure 14 shows the distribution of daily shiftable electricity consumption by hot
water tank (in kWh) across different seasons and weekday/weekends. Some key takes are:
Majority of the household (~90%) can potentially provide at least 40% of their daily
electricity consumption by hot water tank through their excess PV generation
throughout the year. In summer and spring, majority of the households can provide
up to 70% of their daily electricity consumption by hot water tank.
Highest volume of shiftable electric hot water load is during winter. This is because
of the increased hot water load and although there is lower level of excess PV
generation, it can still provide a good volume of the electricity consumption by hot
water tank. Lowest volume of shiftable electric hot water load is during summer
since, although there is high excess PV generation, hot water requirement is much
smaller compared to other seasons.
On average there is around 4.3 kWh of shiftable electric hot water load per
household per day. As seen in Figure 14, this value can be much smaller or larger for
some households according to their excess PV generation, electricity consumption
by hot water tank and seasons.
On average per household, daily excess PV can provide 73% of the daily electricity
consumption by hot water tank
Figure 13 Percentage of shiftable electric hot water load across different seasons and
weekend/weekdays
Figure 14 Shiftable electric hot water load in kWh across different seasons and weekday/weekends
The following two figures investigate the percentage and volume of daily excess PV
generation that is left after providing the potential electric hot water heating.
For majority of the households there is small amount of excess PV left after
providing electricity consumption by hot water tank during winter.
In spring and summer, there is more than half of excess PV generation left for at
least half of the households.
On a yearly average, there is still considerable volume of daily excess PV generation
left after providing electricity consumption by hot water tank. On average per
household, there is 5kWh of daily excess PV energy left after providing the hot
water load. This amount is much less during winter.
Figure 15 Percentage of daily excess PV left after providing the potential electricity consumption by
hot water tank
Figure 16 Amount of excess PV left after providing potential electricity consumption by hot water tank
across seasons and weekend/weekdays
3.1.3 Economic analysis
This section analyses the potential financial benefits of shifting electric hot water load to
peak excess solar periods. Economic analysis is carried for a range of different tariff
arrangements. For this analysis, the most important variables are the $ paid for heating
water (c/kWh) and the opportunity cost of using excess PV on hot water (PV export rate-
c/kWh). The analysis is carried for control 1, control 2 and continuous hot water connection
arrangements.
It is important to note that the retailer tariff offers will be updated in July 2019, so
this analysis is only indicative of the current arrangements.
Another important point is, with the increasing levels of rooftop PV installations,
there is an anticipated decrease in PV export rates. This will likely make the use of
excess PV on hot water load more valuable in the future.
The tariffs used for the analysis are extracted from major retailer tariffs found on
‘Energymadeeasy’ website for NSW, QLD and SA states. A total of 80 different tariff offers
are studied for finding the distribution of potential savings.
3.1.3.1 Controlled load 1(Tariff 31) households
There are 75 households with control load 1/Tariff 31 (10pm-7am). For these households:
There are negative or no financial benefit in shifting control hot water load to solar
times if Tariff PV_export>= Tariff Control load
There are financial benefits for customers in shifting control hot water load to solar
time if Tariff PV_export< Tariff Control load
Note that non-of the analysed tariff offers had Tariff PV_export>Tariff Control load 1 (only a few
offers where Tariff PV_export=Tariff Control load 1). It was also found that the difference between
Tariff PV_export and Tariff Control load 1 was smaller for NSW region tariffs with an average of
3c/kWh , medium for QLD tariffs with an average of 8.5c/kWh and higher for SA tariffs with
an average of 11.6c/kWh. Therefore, $/kWh savings from shifting hot water load are smaller
for households located in NSW region followed by QLD and SA.
Through analysing the tariffs, the distribution of annual potential savings is demonstrated in
below Figure 17 for the 75 control 1 households for four different tariff categories*.
Tariff_A= Tariff Control load 1 - Tariff PV_export = 3c/kWh (most retailer offers in NSW)
Tariff_B= Tariff Control load 1 - Tariff PV_export = 5c/kWh
Tariff_C= Tariff Control load 1 - Tariff PV_export = 8c/kWh (most retailer offers in QLD)
Tariff_D= Tariff Control load 1 - Tariff PV_export = 10c/kWh (most retailer offers in SA)
* Note that, below savings represent the theoretical maximum values according to the identified shiftable electricity consumption by hot water
tank because the calculations don’t consider the potential inefficiencies may be caused by the method of shifting the hot water load (i.e. simple
timer, diverter, smart switc h etc.).
Figure 17 Distribution of potential annual savings through shifting hot water load to solar times for
the analysed four tariff brackets for control 1 households
It is seen that for typical NSW tariffs offers where the difference between Tariff
PV_export and Tariff Control load 1 are smaller, the average annual saving is around
$ 48/year.
For QLD customers, average potential savings are around $ 120 per year for the
control load households.
For SA customer, average potential savings are around $ 145 per year for the control
load households.
3.1.3.2 Controlled load 2 (Tariff 33) households
There are 127 households whose hot water is in control load 2 connection. The Tariff Control
load 2 offers are around 4c/kWh greater than the Tariff Control load 1 (due to increased availability
of hot water heating). Taking this into account the potential savings for control 2 households
are analysed for the below categories of tariffs:
Tariff_X= Tariff Control load 2 - Tariff PV_export = 7c/kWh
Tariff_Y= Tariff Control load 2 - Tariff PV_export = 10c/kWh
Tariff_W= Tariff Control load 2 - Tariff PV_export = 12c/kWh
Tariff_Z= Tariff Control load 2 - Tariff PV_export = 15c/kWh
Figure 18 Distribution of potential annual savings through shifting hot water load to solar times for
the analysed four tariff brackets for control 2 households
As expected, potential savings are greater for control load 2 households compared
to the control load 1 households.
For tariff X (typical NSW offer), households have an average annual saving of $110.
For tariff W (typical QLD offer), households have an average annual saving of $187.
For tariff Z offers, households have an average annual saving of $234.
3.1.3.3 Continuous tariff households
There are 151 households whose hot water is in continuous connection. The Tariff Con tinuous
which may be under a flat or Time of Use arrangement has generally higher rates that Tariff
Control load 1 and Tariff Control load 2 control 1 and control. Taking this into account the potential
savings for control 2 households are analysed for the below categories of tariffs:
Tariff_1= Tariff Continuous - Tariff PV_export = 10c/kWh
Tariff_2= Tariff Continuous - Tariff PV_export = 13c/kWh
Tariff_3= Tariff Continuous - Tariff PV_export = 16c/kWh
Tariff_4= Tariff Continuous - Tariff PV_export = 20c/kWh
Figure 19 Distribution of potential annual savings through shifting hot water load to solar times for
the analysed four tariff brackets for continuous households
Potential benefits become quite significant for households whose hot water is on
continuous tariff due to the larger difference between Tariffcontinuous and Tariffpv_xport
In fact, for typical NSW tariff offers, households can get on average $161 savings per
year. Potential average savings for typical QLD tariff offers is around $260 per year.
Potential average savings for typical SA tariff offers is $324 per year.
Once again, it is important to mention that though the benefits look promising, possible
costs involved with altering the current controlled load connections and other possible costs
involved in implementing the load shifting algorithm needs to be considered (which will be
discussed by the project team).
This section concludes with giving examples of average daily profiles of households from
different types of hot water connections: control 1, control 2, continuous and houses which
already use solar periods for heating water (most likely via simple timer). Each figure shows
daily load (exc. hot water), PV generation and electricity consumption by hot water tank in 5
minutely resolution.
Ex 1: Controlled load 1 household (small potential saving)
Ex:2 Controlled load 1 household (medium potential saving)
Ex 3: Controlled load 2 household (medium potential saving)
Ex 4: Controlled load 2 household (high potential saving)
5 minutely average Power (W)
5 minutely average Power (W)
5 minutely average Power (W)
5 minutely average Power (W)
Ex 5: Continuous household (high potential saving)
Ex 6: Continuous household (medium potential saving)
Ex 7: Continuous household with hot water during solar hours (simple timer)
Ex 8: Continuous household with hot water during solar hours
Figure 20 Example Average daily profiles from selected households with control1, control 2, continuous connections
5 minutely average Power (W)
5 minutely average Power (W)
5 minutely average Power (W)
5 minutely average Power (W)
3.2 Network’s perspective
This section analyses the potential implications of the electric hot water load shifting from
the network’s perspective. In particular, the focus is on the potential reduction of excess PV
generation when the load shifting is implemented in aggregate. This can help DNSPs in
dealing with over voltages issues due to high levels of exported rooftop PV. Moreover,
aggregate electric hot water load control can also reduce network peak demand*.
It should be emphasized that the strategy of using electric hot water load to reduce
exported rooftop PV generation have been discussed in two DNSPs recent reports. SAPN and
Energex (Queensland) have named this strategy as ‘Solar Sponge’ and mentioned that there
would be trials happening between 2017-2020. Energex has a new cost reflective tariff offer
called ‘NTC 7300’ which encourages households to shift their electricity intensive loads such
as electric hot water, AC, pool pump and EV to the times of high excess PV. In return, the
households will be rewarded with lower rates such as control load (Energex, 2019b) (SA
Power Networks, 2014). These cheaper controlled appliance tariff offers may reduce the
financial value of 3rd party solutions for load control algorithms. Even though these trials are
currently taking place, there are not much detailed information about them. DNSPs also
mention that they would be open to receive 3rd party solutions to help them in
implementing their strategy (Ausgrid, 2016) (SA Power Networks, 2014).
Question 2: Considering the load shifting to peak solar times are already in two DNSPs
agenda (and more likely to be taken by other DNSPs as well over time), how will this
impact Solar Analytics’ value propositions to networks with regards to its load shifting
capabilities?
As the DNSPs are already aware of the potential of electric hot water load shifting and have
carried research previously for quantifying the benefits (Swinson, Hamer, & Humphries,
2015), this section only presents a preliminary analysis. Two case studies analyse the
potential impact of aggregate load shifting in QLD and SA. For simplicity, the case studies
assume the analysed Solar Analytics households are representative of their respective states.
Question 3: Is there a value of carrying a deeper case study analysing the entire Solar
Analytics fleet and the impacts of aggregate load shifting algorithm? This more detailed
case study can look into spatial characteristics of voltage and aggregate load shifting and
may build a representative model which studies the relationship between LV voltage and
reduction in excess PV generation (the impact of aggregate load shifting).
For the case studies, South Australia and Queensland are chosen because rooftop
penetration levels are around 31-33% which is much greater than NSW (%17). Also, Solar
Analytics have most of its customers in Queensland and South Australia.
* Ausgrid reports electric hot water units that are on continuous connection contributes to network peak demand (~6 in winter
and ~3 in summer) (Ausgrid, 2016). If desired, a case study can target Solar Analytics customers whose connections are on
continuous tariffs and measure the impact of peak demand reduction through the load shifting algorithm. Furthermore,
Energex has achieved 13.6MVA of peak reduction through the control of electric hot water during its trial (Energex, 2014).
3.2.1 Case Study for South Australia (SA) Households
Through using the analysed households in South Australia, a network level generalization is
made. As a preliminary analysis, the PV generation, load and electricity consumption by hot
water tank is scaled up to:
In SA there are around 250,000 rooftop systems with solar with peak PV capacity
970 MW (Clean Energy Regulator, 2019)
In SA there are around 300,000 houses with electric water heating (SA Power
Networks, 2014)
The focus is given on the quantifying the potential reduction of solar exports via electric hot
water load shifting. The first analysis is focused on the 16th of November 2018 which had the
highest aggregate excess PV generation for the analysed households. The second example
day focuses on the 23rd of June with the highest electricity consumption by hot water tank.
Below Figure 21 shows the excess PV and electricity consumption by hot water tank before
and after load shifting and the resultant reduction in excess PV. It is seen that:
On the 16th of November through electric hot water load shifting, the peak excess PV
power can be reduced from 680MW to 480MW (around 30% reduction). The daily
total exported PV generation is also reduced around 25%. This may greatly help with
over voltage problems.
On the 23rd of June, aggregate electricity consumption by hot water tank is greater
than the aggregate exported PV. Shifting electric hot water load reduces the excess
PV generation significantly on this day (more than 75%).
Yet, it is not very clear whether load shifting would be much of an interest to networks
during winter (or days with less excess PV and no over-voltage issues). On the other hand,
SAPN also prioritizes the reduction of late night-time peak caused by controlled hot water
which causes one of the highest NEM prices in SA, therefore, electric hot water load shifting
may help this problem not only on the high excess PV days but throughout the year.
16th of November 2018 (day of highest excess PV) 23rd of June (day of highest electricity consumption by hot
water tank)
Real data inferred to SAPN network
Real data inferred to SAPN network
Hot water shifted to solar peak hours
Hot water shifted to solar peak hours
Excess PV before and after hot water control
Excess PV before and after hot water control
Figure 21 Demonstration of aggregate electric hot water load shifting for SAPN on two dates:
16/11/2018 with highest excess PV and 23/06/2018 with highest hot water load
Below Figure 22 demonstrates the overall potential reduction of daily excess PV generation
in SAPN throughout the year with the hot water load shifting. It is seen that in winter times,
excess PV can be completely eliminated through the successful hot water load shifting.
Furthermore, significant reductions can be achieved during all other seasons
Figure 22 SAPN aggregate daily excess PV generation across the year: before and after aggregate
electric hot water load shifting
3.2.2 Queensland case study
Through using the analysed households in QLD, a network level generalization is made. As a
preliminary analysis, the PV generation, load and electricity consumption by hot water tank
is scaled up to:
In QLD there are around 600,000 rooftop systems (penetration around 33%)
Energex reports that it controls 65% of its customers’ electric hot water, ~
770,000 households (Energex, 2014). Since there was no relevant info from
ERGON, Energex’s report is taken as reference for this part. As a result, there are
estimated 1,170,000 hot water systems in QLD.
The focus is given on the potential reduction solar exports during peak solar hours. The first
analysis is focused on the 23rd of November 2018 which had the highest aggregate excess PV
generation for the analysed households. The second example day focuses on the 17th of June
with the highest electricity consumption by hot water tank. Below Figure 23 shows the
excess PV and electricity consumption by hot water tank before and after load shifting and
the resultant reduction in excess PV.
On the 23rd of November through electric hot water load shifting, the peak excess PV
power can be reduced from 1450 MW to 980 MW (around 32% reduction). The daily
total exported PV generation is also reduced around 35%.
On the 17th of July, as aggregate electricity consumption by hot water tank is greater
than excess PV generation, excess PV can almost be eliminated through the load
shifting (more than 90% reduction)
23rd of November 2018 (day of highest excess PV) 17th of June (day of highest electricity consumption by
hot water tank)
Real data inferred to QLD network
Real data inferred to QLD network
Hot water shifted to solar peak hours
Hot water shifted to solar peak hours
Excess PV before and after hot water control
Excess PV before and after hot water control
Figure 23 Demonstration of aggregate electric hot water load shifting for QLD network on two dates:
23/11/2018 with highest excess PV and 17/07/2018 with highest hot water load
The following Figure 24 demonstrates the overall potential reduction of daily excess PV
generation in QLD throughout the year with the hot water load shifting. It is seen that in
winter and autumn periods times, excess PV can be eliminated through the successful hot
water load shifting. Furthermore, significant reductions can be achieved during summer and
spring.
Figure 24 QLD aggregate daily excess PV generation across the year: before and after aggregate
electric hot water load shifting
4 Conclusions & Future work
This report has analysed the potential economic benefits for the households from shifting
electric hot water load to the times of excess PV generation. It is found that the benefits
highly depend on household’s electric hot water connection arrangement such that, control
1 households have smaller potential savings due to smaller difference between the hot
water heating rates and solar export rates. On the other hand, households with the
continuous electric water heating can have more significant savings since the difference
between the continuous tariff and solar export rates are greater. Moreover, the benefits are
smaller for NSW households followed by QLD and higher for SA households because of the
differences of the current retail offers between the states. It should be emphasized that
these benefits can drastically change according to upcoming changes in retail offers and new
control load offers being trialled by Energex and SAPN under the ‘Solar Sponge’ strategy
such as the discussed NTC 7300 tariff offer. Furthermore, there needs to be a cost analysis
associated with the required modifications of households current electric hot water circuit
for installing Solar Analytics’ future load control product. This will help to calculate the
return of the product and decide which customers can have tangible financial benefits.
Based on the comments received for this report, future hot water load control work may
include:
Choosing a method for shifting electric hot water and analyse potential costs, losses
and inefficiencies through the chosen method in load shifting:
o Simple timer
o Diverter
o Smart switching (custom Solar Analytics algorithm)
Expand network level case studies, analyse voltage implications of hot water load
shifting on the network level.
Carry specific case-study for Ergon guided by project partner’s specific requests and
questions.
One of the important discussions moving forward in the project is to decide which appliance
to target first for the load shifting. Or alternatively, the implementation can go in parallel,
depending on the development capacity. To start the conversation, below Table 3
summarizes a few key design and implementation aspects comparing electric hot water and
air conditioning (AC). Note that this table only reflects the author’s views and will be
updated and expanded as more opinions and comments are received from the participants.
The colours indicate the comparative strength (advantage) and weakness (disadvantage)
from levels 1 to 5: red corresponds to 1 (weak) and green corresponds to 5 (strong).
Table 3 Comparison of electric hot water vs. AC load shifting aspects
Aspect
Electric
hot water
HVAC
Comments
Penetration level
4
5
Electric hot water heating has penetration levels between 50-60%
across Australia. AC has higher penetration anywhere between 60-
70% Australia wide.
Concerns for the
customer comfort
4
2
Households generally don’t think about their hot water system so long
as there is enough hot water for shower. Households can care a lot
about their thermal comfort on hot/humid days.
Applicability
throughout the year
5
3
Although it varies according to season, households need hot water
throughout the year. Unless they own a reverse cycle AC, households
only use HVAC during hot summer days. Need more info on the use of
AC for heating purposes
Energy efficiency of
the load shifting
4
1
Most Australian households have poor insulation which means pre-
cooling/heating can result in a lot of waste energy. Hot water tanks
have generally good insulation hence more effective with preserving
the stored thermal energy.
Annual economic
benefits ($/year) for
households
4
3
Hot water shifting will have limited benefits for control load 1
customers and more substantial benefits for control load 2 and
continuous households. Yet, the benefits of hot water load shifting will
be year round. HVAC can bring higher savings per kWh as more units
are on continuous tariffs. However, majority of savings will be limited
to hot summer days (assuming AC is mostly used for cooling).
Network peak
demand/ demand
response
3
5
HVAC is the perfect appliance for peak demand reduction as it is the
main driver for the annual peak demand in most networks. Hot
water’s benefits for peak demand reduction is less, however it was still
found valuable as demonstrated by Ausgrid and Energex trials.
Network voltage
issues/ excess PV
4
5
Both appliances can be highly valuable for reducing excess PV within
the network. HVAC shifting is more valuable for summer days with
higher excess PV and peak demand as hot water demand is minimal
during these days
. Hot water shifting can be more valuable during
spring season where there is still high excess PV
Implementation
challenges
4
3
In general, hot water control is a more mature area: network
ripple/timer control, 3rd party solutions (diverter, simple timer) etc. AC
control is more recently being trialled more seriously and only a
handful of serious 3
rd
party solutions are available to date.
References
Ausgrid. (2016). Hot Water Load Control Trials. https://doi.org/10.1016/s1474-
6670(17)37118-5
Ausgrid. (2018). Network Price Guide. Retrieved from https://www.ausgrid.com.au/-
/media/Files/Network/Documents/ES/ES7.pdf
Clean Energy Regulator. (2019). PV penetration across Australia. Retrieved from
http://www.cleanenergyregulator.gov.au/
Endeavour Energy. (2018). Network Price List 2018/2019.
Energex. (2014). Demand Management Program 2015-2020, (October).
Energex. (2019a). Annual Pricing Proposal 2019-2020.
Energex. (2019b). Energex TSS Explanatory Notes 2020-2025.
Ergon Energy. (2016). Revised Tariff Structure Statement 2017 to 2020.
SA Power Networks. (2014). SA Power Networks: Flexible load strategy. Retrieved from
https://www.aer.gov.au/system/files/SAPN - 20.34 PUBLIC - SAPN Flexible Load
Strategy.pdf
SA Power Networks. (2017). Revised Tariff Structure Statement 2017-2020. Retrieved from
https://www.sapowernetworks.com.au/public/download.jsp?id=9716
Swinson, V., Hamer, J., & Humphries, S. (2015). Taking demand management into the future:
Managing flexible loads on the electricity network using smart appliances and
controlled loads. Economic Analysis and Policy, 48, 192203.
https://doi.org/10.1016/j.eap.2015.11.002
Appendix. A
Data cleaning
Initially 622 households are identified with control load for electric hot water during
which had at least 1-year data (March 2018- March 2019).
Number of households with less than 5% missing data (Load, PV or HW): 450
Next, sites with regular hot water usage are identified. In some cases households
had zero HW consumption, or very irregular use. Also some sites with insensible
measurements are also eliminated. Remaining number of households: 358
5 of the sites were commercial so they are further eliminated: 353
A further analysis had to be conducted for identifying the household’s whose
‘ac_load_net’ included electricity consumption by hot water tank. When this was the
case, electricity consumption by hot water tank was removed from the ac_load_net.
Please see below guidelines on how to identify the sites whose load included
electricity consumption by hot water tank:
Checking two things:
o Is Hot_Water (kW) > Load (kW) + 100W (for potential noise) at any point in
time (indicating load wouldn’t include HW). And for how many instances? If
there are at least 50 instances load doesn’t include electric hot water
else
o Check correlation between load and hot water throughout the year.
Especially find days where load is at minimal levels. It is not very easy to set
a threshold correlation value, but 0.3 found to be sufficient after
experimentation.
Appendix. B
Average daily seasonal and weekend/weekday statistics
Average seasonal & weekday/weekend
Daily Load ( kWh)
(exc hot water)
Daily Hot
water (kWh)
Daily PV
(kWh)
Daily Excess
PV (kWh)
Daily self-consumption
rate (%)
Autumn weekday
15.4
5.5
13.4
8.3
42.9
Autumn weekend
16.9
5.6
14.2
8.1
46.1
Winter weekday
16.1
7.5
12.8
8.2
40.1
Winter weekend
16.8
7.6
12.6
7.5
44.6
Spring weekday
15.1
5.9
17.2
11.2
38.3
Spring weekend
15.7
6.1
17
10.7
41.6
Summer weekday
21.6
4.4
18.7
10
47.7
Summer weekend
22.5
4.5
18.9
9.9
51
Appendix. C
Energex hot water switching trial on a substation (two days with similar temperature, on
one day, conventional audio frequency load control (AFLC) is implemented, on the other
day ‘Solar Sponge’ is implemented, which results from shifting the AFLC times (Swinson et
al., 2015)
Appendix. D
Algorithm improvements & cautions for future work
Identification of load circuits which include hot water with higher accuracy!
Some household loads mirrored hot water load on the x axis (negative
measurements) when they were wrongly identified as including the hot water (and
hence hot water was subtracted from the load circuit)
Control load 2 times vary for households (5-7pm most commonly restricted period
but other times it varies to 5.30-7.30pm, 6-8pm etc.)
Important caution is for hot summer days! As the daily temperature would heat the
tanks up it may cause tanks to soak up less energy than the anticipated amount
(referenced from the regular night-time heating). (physical modelling may be useful)
Another important consideration is that changing the hot water heating schedule
may cause running out of hot water especially on very cold days, which needs
caution!
ResearchGate has not been able to resolve any citations for this publication.
Article
Unprecedented changes have occurred over the last five years in the way customers use electricity. These changes are driving electricity distributors to evolve and extend their demand management capabilities to include grid balancing, respond to localised demand and promote and activate smart appliances. In South East Queensland, Australia, two successful forward looking demand management programs are well established. More than 50,000 demand response ready air conditioners have been connected to the network and are able to be controlled by the distributor. Results show that demand reductions from these air conditioners are reliable and sustained for the period of demand events. A second program uses controlled load electric hot water systems as a 'solar sponge' to integrate renewables into the network. This article highlights the potential demand management benefits of using hot water systems to reduce the localised peaks and fill the midday demand trough. The results from both programs show the capability of these demand management tools to improve network utilisation and grid balancing and reduce overall network expenditure. A further demand management initiative identified as having the greatest likelihood of success in delivering benefits to both the utility and customer are tariff structures which incorporate cost reflective pricing. In this way, time of use and magnitude of demand are addressed and positive price signals encouraging load control of appliances are provided. This coupling of demand management and tariffs is shown to be highly effective in achieving demand reductions. Automated load control can support customers' acceptance of new pricing approaches and provide a 'set and forget' solution for optimising the benefits of cost reflective tariffs. The challenge for distributors is how to transition the existing demand management incentives and tariffs to a sustainable future program in an increasingly disaggregated and competitive market.
Hot Water Load Control Trials
  • Ausgrid
Ausgrid. (2016). Hot Water Load Control Trials. https://doi.org/10.1016/s1474-6670(17)37118-5
PV penetration across Australia
Clean Energy Regulator. (2019). PV penetration across Australia. Retrieved from http://www.cleanenergyregulator.gov.au/ Endeavour Energy. (2018). Network Price List 2018/2019.
Demand Management Program
  • Energex
Energex. (2014). Demand Management Program 2015-2020, (October).
Annual Pricing Proposal
  • Energex
Energex. (2019a). Annual Pricing Proposal 2019-2020.
Energex TSS Explanatory Notes
  • Energex
Energex. (2019b). Energex TSS Explanatory Notes 2020-2025.
Revised Tariff Structure Statement
  • Ergon Energy
Ergon Energy. (2016). Revised Tariff Structure Statement 2017 to 2020.
SA Power Networks: Flexible load strategy
  • Sa Power Networks
SA Power Networks. (2014). SA Power Networks: Flexible load strategy. Retrieved from https://www.aer.gov.au/system/files/SAPN -20.34 PUBLIC -SAPN Flexible Load Strategy.pdf
Revised Tariff Structure Statement
  • Sa Power Networks
SA Power Networks. (2017). Revised Tariff Structure Statement 2017-2020. Retrieved from https://www.sapowernetworks.com.au/public/download.jsp?id=9716